There has long been a demand for the ability to describe the differences between two data sets. The data sets may be completely independent of each other or they may be the same data set captured at two different points in time (e.g., two “snapshots” of a file system). The value of such an ability crosses applications. Data backup, Storage Resource Management (SRM), and mirroring are just three of the applications that typically need to efficiently discover and describe the differences between data sets.
Classic backup technologies can describe the changes in a data set, including renames, deletes, creates, and modification of particular elements. However, their methods for finding the changes between the systems are extremely slow. They “walk” (traverse) the entire file system in a breadth-first or depth-first manner, taking advantage of none of the optimized data set differencing tools that internal replication tools can utilize. To reduce backup media consumption and system load, backup applications sometimes run differential or incremental backups, in which they attempt to capture only the data that has changed from the previous backup. However, these differential or incremental backups tend not to run significantly faster than the full-system backup, because discovering and describing the changes takes so long.
SRM tools attempt to capture information about the locus of activity on a system. As with backup applications, finding out what parts of the system are active, usually done by determining what is modified, is extremely slow.
Mirrors have difficulty in resolving changes to both sides of a mirror. In mirroring, the data residing between mirrored systems can diverge when both sides of the mirror can be written. Asynchronous mirrors never have a completely current version of the source data. If the source becomes inaccessible and the mirror is brought online for user modification, each half of the mirror will contain unique data. The same can happen to a synchronous mirror, if both sides are erroneously made modifiable. In either case, to resolve the differences between the divergent mirrors will require discovering and describing those differences to the user.
Thus far, technologists have separated the problems of discovering and describing the changes between two datasets. For example, mirroring applications tend to be extremely efficient at discovering and replicating the changes between versions of a dataset. However, they are incapable of describing those changes at a level that is useful to a human user. For example, they can tell a user which blocks of which disks have been changed, but they cannot correlate that information to the actual path and file names (e.g., “My Documents\2003\taxes\Schwab Statements\July”), i.e., “user-level” information.
Another technique, which is described in co-pending U.S. patent application Ser. No. 10/776,057 of D. Ting et al., filed on Feb. 11, 2004 and entitled, “System and Method for Comparing Data Sets” (“the Ting technique”), can print out the names of files that are different between two data sets. However, the Ting technique does not attempt to describe a potential relationship between those differences. For example, a file may have been renamed from patent.doc to patent_V1.doc. The Ting technique would claim that one data set had a file named patent.doc and the other has a file named patent_V1.doc; however, it would not look more deeply into the problem and declare that patent.doc had been renamed to patent_V1.doc. Understanding the relationships between the differences is a critical aspect of the overall problem. Moreover, the method of describing the changes in the Ting technique is relatively expensive and slow. The Ting technique was designed with the assumption that the differences will be very few, and that processing effort should therefore be expended in quickly verifying the similarities between the two data sets. This assumption does not often hold true in certain applications.
What is needed, therefore, is a technique to quickly and efficiently generate user-level information about the differences between two data sets.